Types of Variables

When we have a table with data, rows correspond to observation units (subjects, etc.) and columns are variables.

There are several types of variables:

Problems with Variables

Also we may have

  • Outliers - too large or too small values, sometimes they are errors, we have to find explanation for them
  • Missing values - not present values, can bias the result
  • Noise - modification of the original value
    • Looks like normal input, but it's faulty
    • Very hard to detect


Types of variables in the analysis:

  • outcome - the variables of our interest
  • explanatory - the variables that are used to analyze and explain the outcome

Types of Relationships

The relationships between the explanatory variable and the outcome

  • independent: there is no association between the variables
  • association: the variables are dependent, but it's not clear what kind of relationship there is
    • causes: changes in the explanatory variables case the outcome to change
    • reverse causation: changes in outcome cause the explanatory variable to change
    • coincidence: just pure chance
    • common cause: some other variable causes both the explanatory variables and the outcome to change - see Lurking Variables and Confounding Variables

Multivariate Analysis

To analyze relationships between variables there are following methods: